We are excited to receive your advice and feedback on our work, which is in its early stages. We would also appreciate links to other initiatives you see as related, so we can make connections and learn from those resources.

I am intrigued by the data sets that you describe in the video. You mention adult learning behaviors in the video. How do you plan on collecting your data sets? I have found that data on why adults engage in informal learning opportunities to be quite sparse. I think your work has the potential to make a great impact in this situation. What kinds of learning environments do you intend to include in your work? 

Hi Chris, thank you for sharing your video! It is exciting to see the work taking place across the AI institutes! There are really interesting intersections across our work at iSAT and AI-ALOE including the design of your virtual assistants (and ways in which they support instructors to create more equitable learning environments). 

Hi Chris, I am Shiran, a co-presenter of the Institute of Student-AI Teaming (iSAT), thanks for sharing your video and sharing the story of your institute! In many ways, I think we face similar challenges. I reach out since we are organizing a workshop where we gather together people who work in such collaborative environments, and in national NSF institute in particular, and are willing to share the challenges they face as part of working in an interdisciplinary team. We would love to hear from lessons learned of simply having you share your experience there. This is the workshop's website: Interdisciplinary Approaches to Getting AI Experts and Education Stakeholders Talking

Thanks for sharing team ALOE! I'm most curious about the ways in which your team and the network of AI researchers that you are cultivating on this project are exploring the online learning dataset. I know you all are in the early stages of this project, but are there there any particular insights or interesting patterns you've found in this data that can share? Or if you're not yet to that stage, can you say a bit about how you're interrogating this data?

And for other machine learning experts visiting this video, what questions or hypotheses would you be interested in exploring with such a data set? 

 

Hi Chris,

Thanks for sharing the overview of your work with AI-ALOE. I look forward to learning more about what you're doing and hopefully have opportunities to connect around the work that AI Engage is doing. If there are writings you'd like to share with educators through the CIRCLS Educator blog, please let me know. 

Good to "see" you and hear what you're doing!

 

Hi Chris! Thanks for sharing and participating. Like so many of the folks in here, I am focused on younger learners. I wonder if you could say more about how your team views adult learners as similar to or different from younger learners? How will that shape the way you build on findings from other AI work, or how we might build on yours?  (I'm part of the Engage AI institute).

Joshua, thanks for asking. In our Institute, we are fortunate to have Professor Ruth Kanfer as our androgogy expert. There are stages within what we consider to be "adults" (age 24 and up), just as "children" have various developmental levels. I and Ruth would be happy to talk with you about what parts of our work can generalize to younger learners.

Interesting work, Chris! I am curious to know more about the feedback you have received from adult learners who have used this model. How effective and user friendly has this program been to adult learners, specifically those who come from marginalized-minority communities or who are under educated? Are there any pre-requisites or basic skills that adult learners must possess prior to using this model?

 Maia, thanks for asking. We are in early implementations of our work, so don't yet have an answer for your question. However, we are focusing our initial development on learners who have the characteristics you describe: students at the Technical College System of Georgia. These studies will help us understand what will be most helpful to design.

Chris, what a great overview of a multifaceted project.  As others have shared, there is so much potential.  I find myself wondering about the datasets and their structure.  You mentioned still experimenting with their structure.  Could you share any surprising insights or challenges from figuring out how to structure them.  I'm asking from a place where I see a dialectic relationship between what they tell use and how we have to format data going in.  Knowing the structure shapes the data we collect/how what we have matters while what we find out makes new things matter (I think).  Thanks!

Hi Chris & all (lots of familiar faces in this discussion!),

This is a very interesting and ambitious project that prompts quite a few possible discussions.

I'm curious what questions/hypotheses others would pose for large datasets that are introduced in the video. Chris, can you provide any more detail about what categories of information those datasets will contain?

As well, what privacy and/or safety agreements and concerns might there be while assembling, storing, and making use of such datasets?

On the virtual teaching assistant front, I know you will be thinking hard about how to find the right balance between things humans can do uniquely or better than virtual assistants (for now) vs. the pieces an assistant can do which free up human instructors to do the things they do best.

But how do we determine what falls in which category, and what's more, how do we design systems that feed information from the assistant to human instructors to improve their interactions with learners? What do others think?

Such great opportunities for study!

Keep it up!

Many of your questions center on how we are handling large datasets, which is a crucial component of our work. I am not an expert on this part of the Institute. For more detail, please contact our dataset expert: Professor Harshvardhan D Sikka <hsikka3@gatech.edu>. Thanks for your interest!